Proceedings of the 2015 International Conference on Electrical, Automation and Mechanical Engineering

Lithium-Ion Battery Capacity Prediction Using Recursive Least Squares with Forgetting Factor

Authors
Z.W. Zhou, Y.D. Lu, Y. Huang, Z.Y. Shi, X. Li
Corresponding Author
Z.W. Zhou
Available Online July 2015.
DOI
10.2991/eame-15.2015.159How to use a DOI?
Keywords
lithium-ion battery; capacity prediction; recursive least squares with forgetting factor; linear degradation model
Abstract

How to predict capacity for lithium-ion battery is one of the most important problems in the field of battery health management. To make the newest data more efficiently, this paper proposes recursive least squares with forgetting factor to estimate the coefficients of the linear capacity degradation model, and presents the adaptive capacity prediction based on the estimation result. The experiment example demonstrates the effectiveness of the proposed approach

Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2015 International Conference on Electrical, Automation and Mechanical Engineering
Series
Advances in Engineering Research
Publication Date
July 2015
ISBN
978-94-62520-71-4
ISSN
2352-5401
DOI
10.2991/eame-15.2015.159How to use a DOI?
Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Z.W. Zhou
AU  - Y.D. Lu
AU  - Y. Huang
AU  - Z.Y. Shi
AU  - X. Li
PY  - 2015/07
DA  - 2015/07
TI  - Lithium-Ion Battery Capacity Prediction Using Recursive Least Squares with Forgetting Factor
BT  - Proceedings of the 2015 International Conference on Electrical, Automation and Mechanical Engineering
PB  - Atlantis Press
SP  - 569
EP  - 572
SN  - 2352-5401
UR  - https://doi.org/10.2991/eame-15.2015.159
DO  - 10.2991/eame-15.2015.159
ID  - Zhou2015/07
ER  -